In the main text, we use MONet to obtain object representations,
because MONet's unsupervised nature allows us to establish our state-of-the-art results using only data from the datasets.
Our method of attention over learned object embeddings, however,
does not rely on MONet representations in particular.
In this section, we show how to apply our method to object detection models that output an object segmentation mask
but not necessarily a feature vector for each object.
This includes, for example,
often-used models such as Mask R-CNN and DETR [1]}, [2]}.
